Job Description :

We are seeking a highly skilled and experienced Lead AI Engineer to drive the design, development, deployment, and optimization of Artificial Intelligence and Generative AI solutions across enterprise applications. The ideal candidate should have a strong background in AI/ML engineering, software development, cloud technologies, and modern AI frameworks, along with the ability to lead technical teams and collaborate with business stakeholders.

The candidate will be responsible for designing and implementing scalable AI-powered solutions that solve complex business problems and improve operational efficiency. This role requires hands-on expertise in Machine Learning, Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), Natural Language Processing (NLP), and AI application development.

The successful candidate will work closely with product owners, data scientists, architects, developers, and business teams to identify AI use cases, define solution architectures, and deliver production-ready AI systems. The role demands a balance of technical leadership, hands-on development, and strategic thinking.

The candidate should have strong programming skills and experience building AI applications using Python and modern AI frameworks. They should be capable of designing APIs, integrating AI models with enterprise systems, and developing scalable microservices-based architectures.

A solid understanding of Large Language Models such as GPT, Claude, Llama, Gemini, or similar models is essential. The candidate should have experience implementing prompt engineering strategies, fine-tuning techniques, vector search solutions, semantic search, and Retrieval-Augmented Generation (RAG) architectures.

The role also involves designing and managing AI pipelines, ensuring model performance, monitoring AI applications in production, and implementing governance, security, and responsible AI practices. Experience with cloud platforms and AI services is highly desirable.

The Lead AI Engineer will mentor junior engineers, establish coding standards, conduct design reviews, and drive best practices across the AI engineering team. Strong communication and stakeholder management skills are essential, as the role requires interaction with both technical and non-technical audiences.

Key Responsibilities :

- Design, develop, and deploy enterprise-scale AI and Generative AI solutions.

- Build and maintain applications using Large Language Models (LLMs).

- Design and implement Retrieval-Augmented Generation (RAG) architectures.

- Develop AI-powered copilots, assistants, chatbots, and intelligent automation solutions.

- Integrate AI services with enterprise applications through APIs and microservices.

- Build scalable backend services using Python and modern frameworks.

- Collaborate with Data Scientists to operationalize machine learning models.

- Monitor and optimize AI model performance, latency, and accuracy.

- Implement security, governance, and compliance controls for AI applications.

- Participate in architecture discussions and technology evaluations.

- Lead technical teams and mentor junior engineers.

- Support client discussions, solution demonstrations, and technical workshops.

- Create technical documentation, architecture diagrams, and deployment guides.

Mandatory Skills :

- Strong experience in Artificial Intelligence and Machine Learning.

- Hands-on experience with Generative AI and Large Language Models (LLMs).

- Strong programming expertise in Python.

- Experience with Retrieval-Augmented Generation (RAG).

- Knowledge of Vector Databases and Embeddings.

- Experience with NLP (Natural Language Processing).

- API Development and Microservices Architecture.

- Cloud Platform experience (Azure, AWS, or GCP).

- Strong software engineering and system design skills.

- Experience leading development teams and mentoring engineers.

Preferred Skills :

- LangChain

- LangGraph

- LlamaIndex

- OpenAI APIs

- Azure OpenAI

- Claude AI

- Hugging Face

- Pinecone

- FAISS

- ChromaDB

- Docker

- Kubernetes

- MLOps

- CI/CD Pipelines

- Prompt Engineering

- Agentic AI Frameworks

- Multi-Agent Systems